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import os
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import os.path as osp
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from paddlevideo.utils.config import get_config
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from paddle.inference import Config, create_predictor
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from utils import build_inference_helper
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class PP_TSMv2_predict(object):
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"""PP-TSMv2模型中常用的参数初始化"""
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def __init__(self,use_gpu=True,ir_optim=True,
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disable_glog=False,save_name=None,enable_mklddn=False,
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precision="fp32",gpu_mem=8000,cpu_threads=None):
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self.use_gpu = use_gpu #是否使用GPU
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self.cpu_threads = cpu_threads #cpu线程数
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self.ir_optim = ir_optim #是否开启IR优化
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self.disable_glog = disable_glog
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self.gpu_mem = gpu_mem #GPU存储大小
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self.enable_mkldnn = enable_mklddn #是否开启mkldnn
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self.precision = precision #mfldnn精度
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self.save_name = save_name #转化推理模型存放名称
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def parse_file_paths(self,input_path: str) -> list:
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"""
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获取模型输入数据
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input_path:模型的输入文件
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"""
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if osp.isfile(input_path):
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files = [
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input_path,
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]
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else:
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files = os.listdir(input_path)
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files = [
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file for file in files
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if (file.endswith(".avi") or file.endswith(".mp4"))
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]
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files = [osp.join(input_path, file) for file in files]
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return files
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def create_paddle_predictor(self,model_f,pretr_p,cfg):
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"""
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创建推理引擎
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model_f:可推理模型存放的路径+配置文件
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pretr_p:训练后的参数存放文件
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cfg:模型配置文件
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"""
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config = Config(model_f,pretr_p)
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if self.use_gpu:
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config.enable_use_gpu(self.gpu_mem,0)
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else:
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config.disable_gpu()
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if self.cpu_threads:
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config.set_cpu_math_library_num_threads(self.cpu_threads)
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if self.enable_mkldnn:
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config.set_mkldnn_cache_capacity(10)
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config.enable_mkldnn()
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if self.precision == "fp16":
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config.enable_mkldnn_bfloat16()
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config.switch_ir_optim(self.ir_optim)
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config.enable_memory_optim()
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config.switch_use_feed_fetch_ops(False)
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if self.disable_glog:
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config.disable_glog_info()
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predictor = create_predictor(config)
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return config,predictor
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def create_inference_model(self,config,model_f,params_f):
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"""
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创建推理模型以及引擎
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config：模型配置文件
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model_f：可推理模型的存放路径
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params_f：可推理模型的参数
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"""
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cfg = get_config(config, overrides=None, show=False)
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InferenceHelper = build_inference_helper(cfg.INFERENCE)
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_, predictor = self.create_paddle_predictor(model_f, params_f, cfg)
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return InferenceHelper,predictor
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def predict(self,config,input_f,batch_size,predictor,InferenceHelper):
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"""
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推理模型,对数据进行推理、预测
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config :PP-TSMv2模型的配置文件
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input_f:待推理数据集的存放路径
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batch_size:模型推理中所取数据的多少,default = 1
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predictor:推理引擎
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InferenceHelper:推理模型
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"""
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result = {}
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cfg = get_config(config, overrides=None, show=False)
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model_name = cfg.model_name
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print(f"Inference model({model_name})...")
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# get input_tensor and output_tensor
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input_names = predictor.get_input_names()
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output_names = predictor.get_output_names()
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input_tensor_list = []
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output_tensor_list = []
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for item in input_names:
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input_tensor_list.append(predictor.get_input_handle(item))
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for item in output_names:
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output_tensor_list.append(predictor.get_output_handle(item))
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files = self.parse_file_paths(input_f)#input_path=input_f
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batch_num = batch_size
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for st_idx in range(0, len(files), batch_num):
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ed_idx = min(st_idx + batch_num, len(files))
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#输出数据预处理
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batched_inputs = InferenceHelper.preprocess_batch(files[st_idx:ed_idx])
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for i in range(len(input_tensor_list)):
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input_tensor_list[i].copy_from_cpu(batched_inputs[i])
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#推理引擎开始推理
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predictor.run()
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batched_outputs = []
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for j in range(len(output_tensor_list)):
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batched_outputs.append(output_tensor_list[j].copy_to_cpu())
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#输出推理结果
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res = InferenceHelper.postprocess(batched_outputs,False,True)
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result["video_id"] = res[0]["video_id"]
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result["topk_class"] = res[0]["topk_class"].tolist()[0]
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result["topk_scores"] = res[0]["topk_scores"].tolist()[0]
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print(result)
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return result
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def main():
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config = 'D:/download/PaddleVideo1/output/output/pptsm_lcnet_k400_16frames_uniform.yaml' # 配置文件地址
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input_file='C:/Users/Administrator/Pictures/video_seg_re_hand/test01_3.avi' #待推理数据集存放的地址
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model_file = 'D:/download/PaddleVideo1/output/output/ppTSMv2.pdmodel' # 推理模型存放地址
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params_file = 'D:/download/PaddleVideo1/output/output/ppTSMv2.pdiparams'
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batch_size= 1 #输出推理模型
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infer,predictor = PP_TSMv2_predict().create_inference_model(config,model_file,params_file)
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PP_TSMv2_predict().predict(config,input_file,batch_size,predictor,infer) #推理模型推理、预测
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if __name__ == "__main__":
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main()
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